R (Computer program language)
概要
| 作品: | 373 作品在 203 項出版品 203 種語言 | |
|---|---|---|
書目資訊
A Beginner's guide to GLM and GLMM with R : = a frequentist and Bayesian perspective for ecologists /
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Corpus linguistics and statistics with R = introduction to quantitative methods in linguistics /
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Introduction to data analysis and graphical presentation in biostatistics with R = statistics in the large /
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Data science in R : = a case studies approach to computational reasoning and problem solving /
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Automated data collection with R : = a practical guide to Web scraping and text mining /
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Introduction to deep learning using R = a step-by-step guide to learning and implementing deep learning models using R /
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Statistical analysis of questionnaires : = a unified approach based on R and Stata /
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Learn business analytics in six steps using SAS and R = a practical, step-by-step guide to learning business analytics /
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Business case analysis with R = simulation tutorials to support complex business decisions /
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Pro data visualization using R and Javascript = analyze and visualize key data on the web /
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Statistical methods for environmental epidemiology with R = a case study in air pollution and health /
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Permutation tests for stochastic ordering and ANOVA = theory and applications with R /
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Multivariate nonparametric methods with R = an approach based on spatial signs and ranks /
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An introduction to R for quantitative economics = graphing, simulating and computing /
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Nonparametric hypothesis testing = rank and permutation methods with applications in R /
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Introduction to statistics and data analysis = with exercises, solutions and applications in R /
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Functional programming in R = advanced statistical programming for data science, analysis and finance /
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Big data analytics with R : = utilize R to uncover hidden patterns in your big data /
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Learn R for applied statistics = with data visualizations, regressions, and statistics /
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Advanced R statistical programming and data models = analysis, machine learning, and visualization /
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Quantile regression for cross-sectional and time series data = applications in energy markets using R /
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Measuring productivity in education and not-for-profits = with tools and examples in R /
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Advanced analytics in power BI with R and Python = ingesting, transforming, visualizing /
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R crash course for biologists : = an introduction to R for bioinformatics and biostatistics /
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Supervised machine learning = optimization framework and applications with SAS and R /
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Complex-valued econometrics with examples in R = modelling, regression and applications /
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Applied analytics through case studies using SAS and R = implementing predictive models and machine learning techniques /
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From experimental network to meta-analysis = methods and applications with R for agronomic and environmental sciences /
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Applied multiple imputation = advantages, pitfalls, new developments and applications in R /
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A course on small area estimation and mixed models = methods, theory and applications in R /
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Pricing export credit = a concise framework with examples and implementation code in R /
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Extending Power BI with Python and R : = ingest, transform, enrich and visualize data using the power of analytic languages /
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Beginning data science in R 4 = data analysis, visualization, and modelling for the data scientist /
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Practical business analytics using R and Python = solve business problems using a data-driven approach /
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Applied linear regression for business analytics with R = a practical guide to data science with case studies /
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Functional programming in R 4 = advanced statistical programming for data science, analysis, and finance /
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Regression-based normative data for psychological assessment = a hands-on approach using R /
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Artificial intelligence and machine learning with R = applications in the field of business analytics /
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Health analytics with R = learning data science using examples from healthcare and direct-to-consumer genetics /
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Kernel methods for machine learning with Math and R = 100 exercises for building logic /
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Essentials of Excel VBA, Python, and R.. Volume II,. Financial derivatives, risk management and machine learning
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Data visualization for social and policy research = a step-by-step approach using R and Python /
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